Using OLAP Cubes to Analyze Market Quotes and Develop Strategies
Summary
This article adapts an online analytical processing library for quote data as well as trading records. It separates shared cube components from trade-specific selectors and records, then adds tools for grouping observations by month or weekday, calculating dispersion, quantizing values into configurable bins, and filtering by field values. The aim is to let researchers aggregate quote-derived fields across dimensions such as time and price behavior, inspect patterns, and develop strategy hypotheses before integrating them into an Expert Advisor.
The article describes testing an automatically selected aggregation schedule on EUR/USD hourly data, including a forward period in 2019, and reports that the approach traded profitably in that period. It presents this as evidence that the analysis tool works, while explicitly calling for further study and warning that OLAP is not a turnkey source of profits. The results depend on the chosen fields, aggregation window, and test setup; the article emphasizes using statistics to explore hypotheses and validate them rather than treating an observed pattern as a reliable edge.
Key ideas
- A reusable OLAP engine can be specialized for quote data by separating shared components from trade-specific classes.
- Selectors and aggregators can group data by calendar periods, weekdays, or quantized field values.
- A variance aggregator provides a way to compare dispersion across selected groups and data sources.
- The described EUR/USD hourly test reported profitable forward trading for a selected aggregation window, but needs further analysis.
- OLAP summaries can support strategy hypotheses, but do not guarantee an edge in unpredictable markets.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.